The problem of training neural networks in a distributed way using data aggregated from individual robots can be viewed as a specific instantiation of a more general class of problems referred to as distributed optimization problems. Distributed optimization is the study of algorithms for solving optimization problems where a sum of individual objective functions, which correspond in this case to the individual robots, is optimized using local computation and message passing. This formulation was first proposed in [1]}, and has been of renewed interest since the seminal work [2]} which presented distributed subgradient descent for convex distributed optimization problems. Subsequent research has focused on improving convergence rates [3]} and extending analysis to a broader range of problems including time-varying communication graphs [4]}, and online or streaming convex objectives [5]}. For an overview of the broader distributed optimization literature we direct the reader to the surveys [6]}, [7]}, [8]}.
